While many of the current works focus on revising general deep models with a probabilistic evaluation of their model or prediction, here
we have a different aim: We modify the \(\alpha \) -\(\beta \)  network to be adaptive to changing contexts hidden in data. We relax our expectation of identifying one single optimal model of the \(\alpha \) -\(\beta \)  network; rather, we consider solving for a full distribution over multiple models. The intuition is that many different models might generate relatively similar performance, so it would be better to estimate a distribution over parameters \(p(\mathbf {w})\) , from the output layer of the \(\alpha \)  networks that detects context. This aligns with the basic principal of MEL [1]}, [2]}. Therefore, we equip the \(\alpha \) -\(\beta \)  network with UQ capacity based on the MEL principal by identifying the distribution of the parameters of a statistical model that bears the maximum uncertainty.
